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A MATLAB toolbox was developed both for teachers performing quick experimental demonstrations during lectures and for students practicing measurement and frequency analysis procedures. The conceptual purpose was to support fundamental acoustics courses with contents defined by the DEGA recommendation 102. All implemented functions and parameters are visible at once and quickly adjustable by a GUI without submenus. A user manual is provided with explanations of how to get started and how all implemented functions can be applied. The toolbox probably still contains bugs. All users are welcome to inform the author about their experiences and proposals for improvement. In future it is planned to convert "Acoustics" to the MATLAB app designer format as Mathworks announced GUIDE to be replaced. Useful extensions would be additional tabs containing animations of sound propagation phenomena or sound fields caused by different sources.
Real estate markets are known to fluctuate. The real estate market in Stuttgart, Germany, has been booming for more than a decade: square-meter price hit top levels and real estate agents claim that market prices will continue to increase. In this paper, we test this market understanding by developing and analyzing a system dynamics model that depicts the Stuttgart real estate market. Simulating the model explains oscillating behavior arising from significant time delays and endogenous feedback structures – and not necessarily oscillating interest rates, as market experts assume. Scenarios provide insights into the system's behavior reacting to changes exogenous to the model. The first scenario tests the market development under increasing interest rates. The other scenario deals with possible effects on the real estate market if the regional automotive economy suffers from intense competition with new market players entering with alternative fuel vehicles and new technologies. With a policy run we test market structure changes to eliminate cyclical effects. The paper confirms that the business cycle in the Stuttgart real estate market arises from within the system's underlying structure, thus emphasizing the importance of understanding feedback structures.
Functionally impaired people have problems with choosing and finding the right clothing. So, they need help in their daily life to wash and manage the clothing. The goal of this work is to support the user by giving recommendations to choose the right clothing, to find the clothing and how to wash the clothing. The idea behind eKlarA is to generate a gateway based system that uses sensors to identify the clothing and their state in the clothing cycle. The clothing cycle consists of (one and more) closet, laundry basket and washing machine in one or several places. The gateway uses the information about the clothing, weather and calendar to support the user in the different steps of the clothing cycle. This allows to give more freedom to the functionally impaired people in their daily life.
Entrepreneurs and small and medium enterprises usually have issues on developing new prototypes, new ideas or testing new techniques. In order to help them, in the last years, academic Software Factories, a new concept of collaboration between universities and companies have been developed. Software Factories provide a unique environment for students and companies. Students benefit from the possibility of working in a real work environment learning how to apply the state of the art of the existing techniques and showing their skills to entrepreneurs. Companies benefit from the risk-free environment where they can develop new ideas, in a protected environment. Universities finally benefit from this setup as a perfect environment for empirical studies in industrial-like environment. In this paper, we present the network of academic Software Factories in Europe, showing how companies had already benefit from existing Software Factories and reporting success stories. The results of this paper can increase the network of the factories and help other universities and companies to setup similar environment to boost the local economy.
Today 40 Gbps is in development at IEEE 802.3bq over four pair balanced cabling. In this paper, we describe a transmission experiment of 25 Gbps enabling either a single pair transmission of 25 Gbps over a 30 meter balanced cabling channel, or a 100 Gbps transmission via a four-pair balanced channel. A scalable matrix modeling tool is introduced which allows the prediction of transmission characteristics of a channel taking mode conversion into account . We applied this tool to characterize PCB-channels including the magnetics and PCB for a four-pair 100 Gbps transmission. We evaluated prototype cables and connecting hardware for frequencies up to 2 GHz and beyond. Finally we investigated possible line encoding schemes and provide measurement results of a transmission over 30 m with a data rate of 25 Gbps per twisted pair.
Modern power transistors are able to switch at very high transition speed, which can cause EMC violations and overshoot. This is addressed by a gate driver with variable gate current, which is able to control the transition speed. The key idea is that the gate driver can influence the di/dt and dv/dt transition separately and optimize whichever transition promises the highest improvement while keeping switching losses low. To account for changes in the load current, supply voltage, etc., a control loop is required in the driver to ensure optimized switching. In this paper, an efficient control scheme for an automotive gate driver with variable output current capability is presented. The effectiveness of the control loop is demonstrated for a MOSFET bridge consisting of OptiMOS-T2™devices with a total gate charge of 39nC. This bridge setup shows dv/dt transitions between 50 to 1000ns, depending on driving current. The driver is able to switch between gate current levels of 1 to 500mA in 10/15ns (rising/falling transition). With the implemented control loop the driver is measured to significantly reduce the ringing and thereby reduce device stress and electromagnetic emissions while keeping switching losses 52% lower than with a constant current driver.
The 21st century: an era where emojis and hashtags find their way into every sentence, where taking selfies, live tweeting and mining bitcoin are the norm, and where Insta-culture dictates what we say and do. This is the era into which the digital native was born. With so many changes in every aspect of our lives, how is it that one of the most influential aspects, our education, has remained unchanged? Our education system not only fails to appeal to today’s students, but more importantly, it fails to equip them with the skills required in the 21st Century. It is thus of no surprise that industries feel graduates entering the workplace lack skills in critical thinking, problem solving and self-directed learning. AI, machine learning and big data: Tools and mechanisms we so eagerly incorporate to create smart factories yet are hesitant to use elsewhere. Gamification and games have shown great results in education and training; with most research suggesting a stronger focus on personalization and adaptation. When combined with analytics and machine learning, the potential of games is yet to be realized. A real-time adaptive game would not only always present an appropriate degree of challenge for the individual but would allow for a shift in focus from the recitation of facts, to the application of information filtered to solve the particular problem at hand. South Africa, a country faced with a severe skills gap, could benefit greatly from games. If used correctly, they may just offer a desperately needed contribution toward equipping both current and future employees with the skills needed to survive in the 21st century. This paper explores the feasibility of using such games for enhanced knowledge dissemination and the upskilling of the workforce.
Autonomous navigation is one of the main areas of research in mobile robots and intelligent connected vehicles. In this context, we are interested in presenting a general view on robotics, the progress of research, and advanced methods related to this field to improve autonomous robots’ localization. We seek to evaluate algorithms and techniques that give robots the ability to move safely and autonomously in a complex and dynamic environment. Under these constraints, we focused our work in the paper on a specific problem: to evaluate a simple, fast and light SLAM algorithm that can minimize localization errors. We presented and validated a FastSLAM 2.0 system combining scan matching and loop closure detection. To allow the robot to perceive the environment and detect objects, we have studied one of the best deep learning technique using convolutional neural networks (CNN). We validate our testing using the YOLOv3 algorithm.
Software and system development is complex and diverse, and a multitude of development approaches is used and combined with each other to address the manifold challenges companies face today. To study the current state of the practice and to build a sound understanding about the utility of different development approaches and their application to modern software system development, in 2016, we launched the HELENA initiative. This paper introduces the 2nd HELENA workshop and provides an overview of the current project state. In the workshop, six teams present initial findings from their regions, impulse talk are given, and further steps of the HELENA roadmap are discussed.
The digitization of factories will be a significant issue for the 2020s. New scenarios are emerging to increase the efficiency of production lines inside the factory, based on a new generation of robots’ collaborative functions. Manufacturers are moving towards data-driven ecosystems by leveraging product lifecycle data from connected goods. Energy-efficient communication schemes, as well as scalable data analytics, will support these various data collection scenarios. With augmented reality, new remote services are emerging that facilitate the efficient sharing of knowledge in the factory. Future communication solutions should generally ensure connectivity between the various production sites spread worldwide and new players in the value chain (e.g., suppliers, logistics) transparent, real-time, and secure. Industry 4.0 brings more intelligence and flexibility to production. Resulting in more lightweight equipment and, thus, offering better ergonomics. 5G will guarantee real-time transmissions with latencies of less than 1 ms. This will provide manufacturers with new possibilities to collect data and trigger actions automatically.
More and more power electronics applications utilize GaN transistors as they enable higher switching frequencies in comparison to conventional Si devices. Faster switching shrinks down the size of passives and enables compact solutions in applications like renewable energy, electrical cars and home appliances. GaN transistors benefit from ~10× smaller gate charge QG and gate drive voltages in the range of typically 5V vs. ~15V for Si.
An integrated synchronous buck converter with a high resolution dead time control for input voltages up to 48V and 10MHz switching frequency is presented. The benefit of an enhanced dead time control at light loads to enable zero voltage switching at both the high-side and low-side switch at low output load is studied. This way, compact multi-MHz DCDC converters can be implemented at high efficiency over a wide load current range. The concept also eliminates body diode forward conduction losses and minimizes reverse recovery losses. A dead time resolution of 125 ps is realized by an 8-bit differential delay chain. A further efficiency enhancement by soft switching at the high-side switch at light load is achieved with a voltage boost of the switching node by dead time control in forced continuous conduction mode. The monolithic converter is implemented in an 180nm high-voltage BiCMOS technology. At V IN = 48V, V OUT = 5V, 50mA load, 10MHz switching frequency and 500 nH output inductance, the efficiency is measured to be increased by 14.4% compared to a conventional predictive dead time control. A peak efficiency of 80.9% is achieved at 12V input.
In recent years, significant progress has been made on switched-capacitor DC-DC converters as they enable fully integrated on-chip power management. New converter topologies overcame the fixed input-to-output voltage limitation and achieved high efficiency at high power densities. SC converters are attractive to not only mobile handheld devices with small input and output voltages, but also for power conversion in IoE, industrial and automotive applications, etc. Such applications need to be capable of handling widely varying input voltages of more than 10V, which requires a large amount of conversion ratios. The goal is to achieve a fine granularity with the least number of flying capacitors. In [1] an SC converter was introduced that achieves these goals at low input voltage VIN ≤ 2.5V. [2] shows good efficiency up to VIN = 8V while its conversion ratio is restricted to ≤1/2 with a limited, non-equidistant number of conversion steps. A particular challenge arises with increasing input voltage as several loss mechanisms like parasitic bottom-plate losses and gate-charge losses of high-voltage transistors become of significant influence. High input voltages require supporting circuits like level shifters, auxiliary supply rails etc., which allocate additional area and add losses [2-5]. The combination of both increasing voltage and conversion ratios (VCR) lowers the efficiency and the achievable output power of SC converters. [3] and [5] use external capacitors to enable higher output power, especially for higher VIN. However, this is contradictory to the goal of a fully integrated power supply.
The presented wide-Vin step-down converter introduces a parallel-resonant converter (PRC), comprising an integrated 5-bit capacitor array and a 300 nH resonant coil, placed in parallel to a conventional buck converter. Unlike conventional resonant concepts, the implemented soft-switching control eliminates input voltage dependent losses over a wide operating range. This ensures high efficiency across a wide range of Vin= 12-48V, 100-500mA load and 5V output at up to 15MHz switching frequency. The peak efficiency of the converter is 76.3 %. Thanks to the low output current ripple, the output capacitor can be as small as 50 nF, while the inductor tolerates a larger ESR, resulting in small component size. The proposed PRC architecture is also suitable for future power electronics applications using fast-switching GaN devices.
The power supply is one of the major challenges for applications like internet of things IoTs and smart home. The maintenance issue of batteries and the limited power level of energy harvesting is addressed by the integrated micro power supply presented in this paper. Connected to the 120/230 Vrms mains, which is one of the most reliable energy sources and anywhere indoor available, it provides a 3.3V DC output voltage. The micro power supply consists of a fully integrated ACDC and DCDC converter with one external low voltage SMD buffer capacitor. The micro power supply is fabricated in a low cost 0.35 μm 700 V CMOS technology and covers a die size of 7.7 mm². The use of only one external low voltage SMD capacitor, results in an extremely compact form factor. The ACDC is a direct coupled, full wave rectifier with a subsequent bipolar shunt regulator, which provides an output voltage around 17 V. The DCDC stage is a fully integrated 4:1 SC DCDC converter with an input voltage as high as 17 V and a peak efficiency of 45 %. The power supply achieves an overall output power of 3 mW, resulting in a power density of 390 μW/mm². This exceeds prior art by a factor of 11.
This paper presents an integrated synchronous buck converter for input voltages >12V with 10MHz switching frequency. The converter comprises a predictive dead time control with frequency compensated sampling of the switching node which does not require body diode forward conduction. A high dead time resolution of 125 ps is achieved by a differential delay chain with 8-bit resolution. This way, the efficiency of fast switching DCDC converters can be optimized by eliminating the body diode forward conduction losses, minimizing reverse recovery losses and by achieving zero voltage switching at turn off. The converter was implemented in a 180nm high-voltage BiCMOS technology. The power losses were measured to be reduced by 30%by the proposed dead time control, which results in a 6% efficiency increase at VOUT = 5V and 0.2A load. The peak efficiency is 81 %.
A 20 V, 8 MHz resonant DCDC converter with predictive control for 1 ns resolution soft-switching
(2015)
Fast switching power supplies allow to reduce the size and cost of external passive components. However, the capacitive switching losses of the power stage will increase and become the dominant part of the total losses. Therefore, resonant topologies are the known key to reduce the losses of the power stage. A power switch with an additional resonant circuit can be turned on under soft-switching conditions, ideally with zero-voltage-switching (ZVS). As conventional resonant converts are only efficient for a constant load, this paper presents a predictive regulation loop to approach soft-switching conditions under varying load and component tolerances. A sample and hold based detection circuit is utilized to control the turn-on of the power switch by a digital regulation. The proposed design was fabricated in a 180 nm high-voltage BiCMOS technology. The efficiency of the converter was measured to be increased by up to 16 % vs. worst case timing and by 13 % compared to a conventional hard-switching buck converter at 20 V input voltage and at approximately 8 MHz switching frequency.
DC-DC-converters are used in many different applications. Specifying the switching frequency is the most important parameter to calculate component costs and required space. Especially automotive applications of small brushed- or brushless dc-motors and the increasing number of DC-DC-converters have high requirements on the structual space (low box volume). This is of particular importance for automotive converters for the new 48 V board net. Multiplying the frequency by two will reduce the size of the power inductor by half at a given specification for output-voltage ripple. Smaller power inductors result in reduced losses due to smaller series resistance and parasitic capacitance. Furthermore a larger switching frequency decreases the size of the DC link capacitors. The circuit will get more idealized. However, as the switching losses increase with frequency, a DC-DC-converter can only benefit from these advantages if the switching behavior can be improved.
This paper presents an optimization method to increase switching slope and switching frequency of a 3.6 kW 3-phase step-up converter by separating the design and layout process into two parts. The first part is the power stage which carries the load current. It contains the power inductance and the drain-source-channel of the power MOSFETs. The second part is the driver circuit which contains the driver ICs, the gate resistor and the gate input impedance. While the switching slope was measured to be improved by 50 % , the switching time decreased by 20 %. Hence, the switching frequency of the step-up converter could be increased from 100 kHz to 200 kHz without loss increase. By mounting the driver ICs in a piggyback configuration in close proximity to the power stage, the parasitics could be further reduced significantly and 500 kHz switching frequency could be achieved with 97.5 % efficiency.
A 3D face modelling approach for pose-invariant face recognition in a human-robot environment
(2017)
Face analysis techniques have become a crucial component of human-machine interaction in the fields of assistive and humanoid robotics. However, the variations in head-pose that arise naturally in these environments are still a great challenge. In this paper, we present a real-time capable 3D face modelling framework for 2D in-the-wild images that is applicable for robotics. The fitting of the 3D Morphable Model is based exclusively on automatically detected landmarks. After fitting, the face can be corrected in pose and transformed back to a frontal 2D representation that is more suitable for face recognition. We conduct face recognition experiments with non-frontal images from the MUCT database and uncontrolled, in the wild images from the PaSC database, the most challenging face recognition database to date, showing an improved performance. Finally, we present our SCITOS G5 robot system, which incorporates our framework as a means of image pre-processing for face analysis.